Methods, apparatus, systems and articles of manufacture are disclosed to estimate an audience population. An apparatus includes a characteristic identifier to determine whether respective ones of respondents are associated with a characteristic, a respondent identifier to determine whether the respective ones of the respondents are recaptured based on a comparison of a person identifier corresponding to respondents and a database of previously identified person identifiers, a count determiner to, in response to the respective ones of the respondents exhibiting the characteristic, increase a total capture count by one and, in response to detecting unique instances of the respective ones of the respondents exhibiting the characteristic, increase a unique capture count by one, and a population estimator to, in response to a recapture probability satisfying a threshold, determine a population estimate having the characteristic based on the total capture count, the unique capture count, and a number of available samples.
Legal claims defining the scope of protection, as filed with the USPTO.
2. The apparatus as defined in claim 1, further including an audience sampler to accumulate an audience sample, the audience sample including the respondents.
3. The apparatus as defined in claim 1, wherein the respondent identifier is to identify the person identifier corresponding to the respective ones of the respondents associated with the characteristic.
4. The apparatus as defined in claim 1, further including a recapture probability estimator to estimate the recapture probability of the respondents.
5. The apparatus as defined in claim 1, wherein the population estimator is to determine a seed population estimate having a value greater than the unique capture count.
6. The apparatus as defined in claim 5, wherein the population estimate is based on the seed population estimate.
7. The apparatus as defined in claim 1, wherein the population estimate is a first population estimate, and the population estimator is to determine a second population estimate of the audience population having the characteristic based on the total capture count and the unique capture count.
8. The apparatus as defined in claim 7, further including a sample determiner to determine a number of audience samples to accumulate, the determination based on a comparison between the first population estimate and the second population estimate.
9. The apparatus as defined in claim 8, wherein the sample determiner is to accumulate at least one more audience sample in response to a difference between the first population estimate and the second population estimate satisfying a population estimate threshold.
10. The apparatus as defined in claim 8, wherein the population estimator is to select the first population estimate in response to a difference between the first population estimate and the second population estimate not satisfying a population estimate threshold.
11. The apparatus as defined in claim 1, wherein the characteristic is at least one of viewing an advertisement, viewing media, or purchasing a product.
12. The apparatus as defined in claim 1, wherein the population estimator is to determine the population estimate using iteration until convergence.
14. The non-transitory computer readable medium as defined in claim 13, wherein the instructions, when executed, further cause the at least one processor to accumulate an audience sample, the audience sample including the respondents.
15. The non-transitory computer readable medium as defined in claim 13, wherein the instructions, when executed, further cause the at least one processor to identify the person identifier corresponding to the respective ones of the respondents associated with the characteristic.
16. The non-transitory computer readable medium as defined in claim 13, wherein the instructions, when executed, further cause the at least one processor to estimate the recapture probability of the respondents.
17. The non-transitory computer readable medium as defined in claim 13, wherein the population estimate is a first population estimate, and the instructions, when executed, further cause the at least one processor to determine a second population estimate of a population having the characteristic based on the total capture count and the unique capture count.
18. The non-transitory computer readable medium as defined in claim 17, wherein the instructions, when executed, further cause the at least one processor to determine a number of audience samples to accumulate, the determination based on a comparison between the first population estimate and the second population estimate.
19. The non-transitory computer readable medium as defined in claim 18, wherein the instructions, when executed, further cause the at least one processor to accumulate at least one more audience sample in response to a difference between the first population estimate and the second population estimate satisfying a population estimate threshold.
20. The non-transitory computer readable medium as defined in claim 18, wherein the instructions, when executed, further cause the at least one processor to select the first population estimate in response to a difference between the first population estimate and the second population estimate not satisfying a population estimate threshold.
21. The non-transitory computer readable medium as defined in claim 13, wherein the instructions, when executed, further cause the at least one processor to determine a seed population estimate having a value greater than the unique capture count.
22. The non-transitory computer readable medium as defined in claim 21, wherein the population estimate is based on the seed population estimate.
23. The non-transitory computer readable medium as defined in claim 13, wherein the characteristic is at least one of viewing an advertisement, viewing media, or purchasing a product.
24. The non-transitory computer readable medium as defined in claim 13, wherein the instructions, when executed, further cause the at least one processor to determine the population estimate using iteration until convergence.
26. The method as defined in claim 25, further including accumulating an audience sample, the audience sample including the respondents.
27. The method as defined in claim 25, further including identifying the person identifier corresponding to the respective ones of the respondents associated with the characteristic.
28. The method as defined in claim 25, further including estimating the recapture probability of the respondents.
29. The method as defined in claim 25, further including determining a seed population estimate having a value greater than the unique capture count.
30. The method as defined in claim 29, wherein the population estimate is based on the seed population estimate.
31. The method as defined in claim 25, wherein the population estimate is a first population estimate, and further including determining a second population estimate of the population having the characteristic based on the total capture count and the unique capture count.
32. The method as defined in claim 31, further including determining a number of audience samples to accumulate, the determination based on a comparison between the first population estimate and the second population estimate.
33. The method as defined in claim 32, further including accumulating at least one more audience sample in response to a difference between the first population estimate and the second population estimate satisfying a population estimate threshold.
34. The method as defined in claim 32, further including selecting the first population estimate in response to a difference between the first population estimate and the second population estimate not satisfying a population estimate threshold.
35. The method as defined in claim 25, wherein the characteristic is at least one of viewing an advertisement, viewing media, or purchasing a product.
36. The method as defined in claim 25, further including determining the population estimate using iteration until convergence.
38. The apparatus as defined in claim 37, further including the processor circuitry to execute the instructions to accumulate an audience sample, the audience sample including the respondents.
39. The apparatus as defined in claim 37, wherein the processor circuitry is to execute the instructions to identify the person identifier corresponding to the respective ones of the respondents associated with the characteristic.
40. The apparatus as defined in claim 37, further including the processor circuitry to execute the instructions to estimate the recapture probability of the respondents.
41. The apparatus as defined in claim 37, wherein the processor circuitry is to execute the instructions to determine a seed population estimate having a value greater than the unique capture count.
42. The apparatus as defined in claim 41, wherein the population estimate is based on the seed population estimate.
43. The apparatus as defined in claim 37, wherein the population estimate is a first population estimate, and the processor circuitry is to execute the instructions to determine a second population estimate of the audience population having the characteristic based on the total capture count and the unique capture count.
44. The apparatus as defined in claim 43, further including the processor circuitry to execute the instructions to determine a number of audience samples to accumulate, the determination based on a comparison between the first population estimate and the second population estimate.
45. The apparatus as defined in claim 44, wherein the processor circuitry is to execute the instructions to accumulate at least one more audience sample in response to a difference between the first population estimate and the second population estimate satisfying a population estimate threshold.
46. The apparatus as defined in claim 44, wherein the processor circuitry is to execute the instructions to select the first population estimate in response to a difference between the first population estimate and the second population estimate not satisfying a population estimate threshold.
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June 22, 2020
January 3, 2023
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